DataStax Blog

I was surprised out how easy it was to get started with Cassandra on DataStax: https://www.datastax.com/

Excellent points. To add, and I’m not taking away from the technical effort, but the use of “native graph” is rather misleading. Existing computer architecture cannot represent a graph with sequentialized memory access (1-dimensional). So any representation has to make assumptions and use random memory access patterns. A longer discussion here (obvious bias aside, the other no longer works at DSE): https://www.datastax.com/blog/2013/11/letter-regarding-native-graph-da…

Netflix loves Cassandra, right? [0][1] So could someone describe why it wasn't a great fit for Uber? How come it was easier to invent the wheel in Go compared to cobbling together something with Cassandra/ES/Kafka (or other Java gadgets from the Hadoop ecosystem)? [0]: https://netflixtechblog.com/scaling-time-series-data-storage-part-i-ec… [1]: https://www.datastax.com/resources/video/cassandra-netflix-and-version…

Recommended reads on the native vs non-native topic: * https://www.datastax.com/dev/blog/a-letter-regarding-native-graph-data… (tldr; there is no such thing as a native graph database) * https://neo4j.com/blog/note-native-graph-databases/ (tldr; native graph databases do exist) Regarding Cypher vs Gremlin: serialization could be a thing but what matters among other things are efficient query optimizations, algorithm and (physical) data model. Ultimately, databases are all reading from 1-dimensional spaces (RAM or disk), either randomly or (best) sequentially. If you can colocate vertices with their respective edges, you're fine: this is trivial for graphs with no edges or graphs that form a linear chain. If not, then things start to become fun, especially in a distributed way. This will impact performance; the language, not so much.

For some other helpful intros to Cassandra data modeling, check out [0], [1], and [2]. I've been working on trying to automate the process of coming up with a good data model for systems like Cassandra. You can see some pretty early results here[3] although there's been a lot of progress since then which will hopefully be shareable soon. [0] http://www.datastax.com/dev/blog/basic-rules-of-cassandra-data-modelin… [1] http://www.slideshare.net/nkorla1share/cass-summit-3 [2] www.ebaytechblog.com/2012/07/16/cassandra-data-modeling-best-practices-part-1/ [3] https://dl.acm.org/citation.cfm?id=2602624

https://jobsremotely.com/ - another job board http://www.datastax.com/ - another company that is all remote

You don't really need to know anything about Hadoop Map/Reduce to start using Spark. Spark has its own, more powerful "map-reduce". You need familiarity with one of the storage platforms supported by Spark - currently these are Hadoop File System and Apache Cassandra. The easiest way to play with Cassandra is: 1. grab a copy of DSE (free to test or develop) and install it (download here: http://www.datastax.com/download) 2. launch 'cqlsh', create a Cassandra keyspace and a table and insert a few rows into it 3. launch 'dse spark' and query your data with e.g. sc.cassandraTable("keyspace", "table").toArray Doing it with Apache Cassandra (not DSE) is going to be slightly harder, because besides installing Cassandra, you'll have to set up standalone Spark cluster (see Spark docs), then follow the instructions in README.md of the driver.

You've probably read this, but in case anyone else is considering EBS and cassandra... "EBS volumes are not recommended for Cassandra data volumes." http://www.datastax.com/docs/1.1/cluster_architecture/cluster_planning

Cloud Software Engineer [SF Bay Area or Austin, TX Preferred, Full Time] Distributed Application Engineer - OpsCenter [Anywhere, World, Full Time] Frontend Technical Lead - OpsCenter [Anywhere, World, Full Time] Java Engineer [Anywhere, World, Full Time] Java Engineer (Windows) [Anywhere, World, Full Time] Performance Software Engineer [SF Bay Area or Austin, TX Preferred, Full Time] Software Testing Engineer for Cassandra [SF Bay Area, United States, Full Time] For full list of positions see: http://www.datastax.com/company/careers Tech used: Python, Java DataStax is a commercial entity behind the Apache Cassandra project, the open source NoSQL database widely-aknowledged as the best foundation for tackling the most challenging big data problems.

We are using client-side SSL certificates extensively for API as well as browser-based single-sign-on for 20+ employees on a smattering of Mac/Linux/iPhone/Android devices. Definitely some ramp-up and wonkiness, but it's working well. Also worth noting that infrastructure components like Cassandra [1] and RabbitMQ [2] leverage PKI as well. Checkout our Jenkins client-side SSL cert auth plugin: https://github.com/pantheon-systems/certificate-authentication-for-jen… [1] http://www.datastax.com/documentation/cassandra/1.2/index.html#cassand… [2] http://www.rabbitmq.com/ssl.html

Both MongoDB and MySQL were started by people who had no background in writing databases. They were blazingly fast compared to existing RDBMS, but once they started adding features that actually provided things that people assumed a database should have (primarily in area of consistency) they no longer look that great. What makes MySQL better than MongoDB is that is older and closer to what a database should do. They still have their warts and some of the warts might not be removed, since it would break compatibility. At this point MongoDB does not have anything going for them. Its key benefit was their speed, but as it turned out that was because data was stored mostly in RAM if Mongo crashed or there was a power loss then you most likely would lose significant (possibly all) of your data. Since then they fixed that and make it more reliable, it is worse in performance than a relational database [1] and it also doesn't scale well [2] Essentially NoSQL databases were designed to be simple and without relational features in exchange for scalability and performance. Mongo you get neither. Mongo doesn't even try to benefit from the CAP theorem. It's neither always consistent nor always available [3]. You generally should always use a relational database, because in majority of cases you do have some schema and you expect data to be consistent. NoSQL databases (especially ones that are AP in CAP) are generally good for specialized use cases, things that have no relations and…

REMOTE, wherever DataStax makes the enterprise edition of Cassandra, the NoSQL db for serious applications. We have developers all over the world and a 100% remote working environment. Some of the development is open source (we are the primary contributors to Cassandra core, which is an Apache Foundation project...) I just joined recently; as a senior developer with startup experience, I had lots of offers from various companies. The reasons I chose DataStax were: - 100% remote means I can work from wherever I want (I like to travel) and whenever, within reason, as long as I get my stuff done - the opportunity to work on interesting new technology that is rapidly changing and being adopted by a lot of Fortune 100, etc - they have been vocal about their plans to IPO in the next few years and are growing like crazy Very competitive salary, lots of open positions: Java, Python, JS, I've even seen some Clojure. Also, SDET and technical writing. http://www.datastax.com http://www.datastax.com/company/careers Hit me up, esmith at datastax dot com, if you'd like an intro.

The cost to store a static set of 400k 768-dimension vectors is also $1 a month on Datastax's AstraDB. However, for that $1, AstraDB replicates the data 3x instead of storing it on a single machine. Here is a link to the cost calculator. Note that the calculator includes cost of ingestion, but the article only mentions storage costs, not ingestion costs: https://www.datastax.com/pricing/vector-search?cloudProvider=aws&c… Disclaimer: I work on vectorsearch/AstraDB at DataStax.

This title was a little misleading to me IMO because (maybe my skill issue) I associated "inferencing" with "generation". After reading the article, it seems Pinecone just now supports in-DB vectorization, a feature that is shared by: - DataStax Astra DB: https://www.datastax.com/blog/simplifying-vector-embedding-generation-… (since May 2024) - Weaviate: https://weaviate.io/blog/introducing-weaviate-embeddings (as of yesterday)

Why is the author listing himself as datastax cto? He isn’t according the Wikipedia, my friend who works there, and their company website. https://www.datastax.com/our-people That’s kind of weird

See https://www.datastax.com/our-people/jonathan-ellis Wikipedia lists them as a founder. Perhaps their author bio is outdated, or Wikipedia is. Not sure about your friend.

What are you talking about? The datastax site lists it: > SANTA CLARA, Calif. – September 28, 2020 – DataStax today announced that DataStax Co-Founder and CTO Jonathan Ellis will deliver a keynote address at ApacheCon @Home 2020 https://www.datastax.com/press-release/datastax-co-founder-and-cto-jon… As an aside, I'm an ApacheCon presenter but there was no press release about the hot excitement of my involvement. Maybe next time :)

The tinkerpop writings of Marco Rodriguez are seriously underrated pieces of computer science literature https://www.datastax.com/blog/tales-tinkerpop

sorry to disappoint you but there is a reason Cassandra tried to add LWT using paxos. it mostly work but it’s extremely slow and still have many bugs. https://www.datastax.com/blog/lightweight-transactions-cassandra-20

DataStax also launched Serverless Cassandra (Astra) recently. https://www.datastax.com/products/datastax-astra/pricing https://thenewstack.io/the-serverless-database-you-really-want/ Disclaimer : I work for DataStax.

DataStax had good results testing the new Shenandoah garbage collector with Cassandra 4.0: https://www.datastax.com/blog/apache-cassandra-benchmarking-40-brings-…

> For example, why hasn't Datastax rolled out a cloud offering on AWS yet? Umm... they do (https://www.datastax.com/platform/amazon-web-services). Instacluster also has an offering. Which kind of highlights the problem.

DataStax made a major deal with Google instead: https://www.datastax.com/platform/google-cloud-platform

There's also Datastax Enterprise Graph (commercial) from the team behind Titan after they were acquired by Datastax. https://www.datastax.com/products/datastax-graph https://venturebeat.com/2015/02/03/datastax-acquires-aurelius-the-star…

There were several talks about CDC in general, and 4.0 in particular, at DataStax Accelerate conference: From Netflix: https://www.datastax.com/accelerate/agenda?session=Live-Traffic-Captur… From Yelp: https://www.datastax.com/accelerate/agenda?session=Streaming-Cassandra… From DataStax: https://www.datastax.com/accelerate/agenda?session=Change-Data-Capture… Videos are already published: https://www.datastax.com/accelerate/sessions - you just need to select correct track...

This article does a massive disservice by using the pre-CQL Column Family and Row terminology. While it's the Cassandra data modelling I'm the most accustomed to personally, it causes endless confusion for users who find themselves in the CQL documentation trying to understand how it all maps to Primary Keys, Partition Keys, static columns, etc. This transition has been causing confusion for at least 5 years now, and it appears people are still using the old terminology! https://www.datastax.com/dev/blog/thrift-to-cql3

DataStax | Core Engineer | 100% REMOTE | http://www.datastax.com/company/careers DataStax builds fully distributed database technology inspired by Apache Cassandra to power applications that need massive scale, 100% uptime, and near-linear scalability on commodity hardware. If you're into "low-level" Systems Java, high performance computing, and distributed computing this role is for you; it's a high profile position working on core database technologies in our flagship product, rapidly making a big impact on the entire business. We’re an international, fully remote team of highly independent engineers looking for like-minded, quality-focused and seasoned developers to join our ranks. We welcome strong, reasoned opinions on our team and believe in healthy debate, respect, and collaboration. If you're interested, please reach out to josh dot mckenzie at datastax dot com

Datastax seems to think that it is a good idea: http://www.datastax.com/dev/blog/consensus-on-cassandra